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Instructor: Merlise Clyde
Office: 223 E Old Chemistry Building	Office hours: Mon & Wed 3-4 or by appointment
Phone: 681-8440	Email: clyde@stat.duke.edu
Lecture:: Tuesday - Thursday @ 4:25-5:40, Room 318 Allen Building

TA: Zhenglei Gao
	Office hours: TBA
	Email: zhenglei@stat.duke.edu

The course covers statistical thought and practice through indepth examination of a wide variety of problems. Emphasis is on data collection and management, sampling and design, and exploratory and graphical data analysis. Introduction to Bayesian statistical methods. Computer orientation is provided, with attention toward statistical packages (primarily R/S-Plus) and use of LaTeX, emacs and some Unix tools.

Topics will be selected from:

• data types, data manipulation and analysis, including data sets from a variety of application fields -- see the datasets link
• exploratory data analysis and statistical graphics
• elements of statistical inference using probability models, including basic issues of sampling-theory and Bayesian inference
• models for normal data including ANOVA and regression models
• models for binary and count/categorical data
• introduction to hierarchical models
• introduction to statistical programming environments such as R/S-Plus
• elements of simulation and introduction to Gibbs sampling WinBugs
  

Though the course does not include rigourous development of statistical theory and methods, we will use and review various concepts and methods of inference, so that some familiarity with basic statistics is desirable. Co-registration in STA 213 or recent experience with similar courses is expected. Students will be expected to become familiar with unix tools, including editors such as emacs and document preparation programs such as LaTeX.

Texts:

Statistical Analysis and Data Display (required) by Richard Heiberger and Burt Holland. (online files) (errata) (HH recommended software) (local link to HH files)

An Introduction to R (required) by W.N. Venables, D.M. Smith and the R Development Core Team (paperback version available thru amazon.com)

Students may find the following list of texts and references useful.

Grading:

will be based on weekly homework and a takehome midterm and final. Students may also be requested to make classroom presentations.